Overview

Dataset statistics

Number of variables17
Number of observations123
Missing cells453
Missing cells (%)21.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory137.0 B

Variable types

Numeric5
Text7
Categorical1
DateTime3
Unsupported1

Alerts

type has constant value ""Constant
airdate has constant value ""Constant
id is highly overall correlated with id_embeddedHigh correlation
id_embedded is highly overall correlated with idHigh correlation
airtime has 56 (45.5%) missing valuesMissing
runtime has 2 (1.6%) missing valuesMissing
rating_average has 123 (100.0%) missing valuesMissing
medium has 90 (73.2%) missing valuesMissing
original has 90 (73.2%) missing valuesMissing
summary has 92 (74.8%) missing valuesMissing
id has unique valuesUnique
url has unique valuesUnique
_links_self has unique valuesUnique
rating_average is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-05 19:24:58.501329
Analysis finished2023-08-05 19:25:02.245690
Duration3.74 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2454166.3
Minimum2330733
Maximum2513104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:02.334923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2330733
5-th percentile2417864.1
Q12444845.5
median2458340
Q32463988.5
95-th percentile2475494.9
Maximum2513104
Range182371
Interquartile range (IQR)19143

Descriptive statistics

Standard deviation23227.286
Coefficient of variation (CV)0.0094644304
Kurtosis6.5482626
Mean2454166.3
Median Absolute Deviation (MAD)11106
Skewness-1.620047
Sum3.0186245 × 108
Variance5.3950682 × 108
MonotonicityNot monotonic
2023-08-05T14:25:02.512019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2460191 1
 
0.8%
2457518 1
 
0.8%
2457516 1
 
0.8%
2457515 1
 
0.8%
2456783 1
 
0.8%
2456782 1
 
0.8%
2451943 1
 
0.8%
2451942 1
 
0.8%
2456695 1
 
0.8%
2456694 1
 
0.8%
Other values (113) 113
91.9%
ValueCountFrequency (%)
2330733 1
0.8%
2387354 1
0.8%
2391045 1
0.8%
2393552 1
0.8%
2410543 1
0.8%
2414525 1
0.8%
2417864 1
0.8%
2417865 1
0.8%
2418847 1
0.8%
2420251 1
0.8%
ValueCountFrequency (%)
2513104 1
0.8%
2502106 1
0.8%
2494234 1
0.8%
2494233 1
0.8%
2489303 1
0.8%
2484864 1
0.8%
2475495 1
0.8%
2475494 1
0.8%
2475493 1
0.8%
2475492 1
0.8%

id_embedded
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59577.309
Minimum328
Maximum67120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:02.680134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum328
5-th percentile35397.3
Q159543
median66054
Q366246
95-th percentile66535
Maximum67120
Range66792
Interquartile range (IQR)6703

Descriptive statistics

Standard deviation13988.078
Coefficient of variation (CV)0.23478868
Kurtosis9.7437173
Mean59577.309
Median Absolute Deviation (MAD)481
Skewness-3.077272
Sum7328009
Variance1.9566632 × 108
MonotonicityNot monotonic
2023-08-05T14:25:03.010749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66444 12
 
9.8%
52230 10
 
8.1%
66535 6
 
4.9%
66077 6
 
4.9%
66256 6
 
4.9%
66205 5
 
4.1%
59543 5
 
4.1%
64945 5
 
4.1%
66180 4
 
3.3%
66207 2
 
1.6%
Other values (48) 62
50.4%
ValueCountFrequency (%)
328 1
0.8%
802 1
0.8%
1905 2
1.6%
6147 1
0.8%
24159 1
0.8%
34705 1
0.8%
41628 2
1.6%
44944 1
0.8%
46638 1
0.8%
47119 1
0.8%
ValueCountFrequency (%)
67120 2
 
1.6%
66819 1
 
0.8%
66535 6
4.9%
66444 12
9.8%
66441 1
 
0.8%
66417 2
 
1.6%
66256 6
4.9%
66248 1
 
0.8%
66244 2
 
1.6%
66207 2
 
1.6%

url
Text

UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:03.282035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length108
Median length87
Mean length74.195122
Min length59

Characters and Unicode

Total characters9126
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2460191/stellar-transformation-5x01-episode-53
2nd rowhttps://www.tvmaze.com/episodes/2460192/stellar-transformation-5x02-episode-54
3rd rowhttps://www.tvmaze.com/episodes/2410543/the-wonderland-of-ten-thousands-5x159-episode-335
4th rowhttps://www.tvmaze.com/episodes/2463638/supreme-god-emperor-1x234-episode-234
5th rowhttps://www.tvmaze.com/episodes/2330733/against-the-sky-supreme-1x157-episode-157
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2460191/stellar-transformation-5x01-episode-53 1
 
0.8%
https://www.tvmaze.com/episodes/2472881/when-is-the-son-off-2x07-episode-7 1
 
0.8%
https://www.tvmaze.com/episodes/2410543/the-wonderland-of-ten-thousands-5x159-episode-335 1
 
0.8%
https://www.tvmaze.com/episodes/2463638/supreme-god-emperor-1x234-episode-234 1
 
0.8%
https://www.tvmaze.com/episodes/2330733/against-the-sky-supreme-1x157-episode-157 1
 
0.8%
https://www.tvmaze.com/episodes/2513104/jiu-tian-xuan-di-jue-3x17-episode-109 1
 
0.8%
https://www.tvmaze.com/episodes/2445911/debu-to-love-to-ayamachi-to-1x08-episode-8 1
 
0.8%
https://www.tvmaze.com/episodes/2462259/fall-in-love-again-1x11-episode-11 1
 
0.8%
https://www.tvmaze.com/episodes/2462260/fall-in-love-again-1x12-episode-12 1
 
0.8%
https://www.tvmaze.com/episodes/2454338/the-silence-of-the-monster-1x23-episode-23 1
 
0.8%
Other values (113) 113
91.9%
2023-08-05T14:25:03.736842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 781
 
8.6%
- 697
 
7.6%
/ 615
 
6.7%
s 599
 
6.6%
t 541
 
5.9%
o 518
 
5.7%
w 403
 
4.4%
p 363
 
4.0%
i 346
 
3.8%
a 313
 
3.4%
Other values (30) 3950
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6050
66.3%
Decimal Number 1395
 
15.3%
Other Punctuation 984
 
10.8%
Dash Punctuation 697
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 781
12.9%
s 599
 
9.9%
t 541
 
8.9%
o 518
 
8.6%
w 403
 
6.7%
p 363
 
6.0%
i 346
 
5.7%
a 313
 
5.2%
m 311
 
5.1%
d 271
 
4.5%
Other values (16) 1604
26.5%
Decimal Number
ValueCountFrequency (%)
2 271
19.4%
1 226
16.2%
4 220
15.8%
3 129
9.2%
0 115
8.2%
5 111
8.0%
7 99
 
7.1%
8 87
 
6.2%
6 79
 
5.7%
9 58
 
4.2%
Other Punctuation
ValueCountFrequency (%)
/ 615
62.5%
. 246
 
25.0%
: 123
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 697
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6050
66.3%
Common 3076
33.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 781
12.9%
s 599
 
9.9%
t 541
 
8.9%
o 518
 
8.6%
w 403
 
6.7%
p 363
 
6.0%
i 346
 
5.7%
a 313
 
5.2%
m 311
 
5.1%
d 271
 
4.5%
Other values (16) 1604
26.5%
Common
ValueCountFrequency (%)
- 697
22.7%
/ 615
20.0%
2 271
 
8.8%
. 246
 
8.0%
1 226
 
7.3%
4 220
 
7.2%
3 129
 
4.2%
: 123
 
4.0%
0 115
 
3.7%
5 111
 
3.6%
Other values (4) 323
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 781
 
8.6%
- 697
 
7.6%
/ 615
 
6.7%
s 599
 
6.6%
t 541
 
5.9%
o 518
 
5.7%
w 403
 
4.4%
p 363
 
4.0%
i 346
 
3.8%
a 313
 
3.4%
Other values (30) 3950
43.3%

name
Text

Distinct74
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:04.045470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length12.365854
Min length4

Characters and Unicode

Total characters1521
Distinct characters75
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)46.3%

Sample

1st rowEpisode 53
2nd rowEpisode 54
3rd rowEpisode 335
4th rowEpisode 234
5th rowEpisode 157
ValueCountFrequency (%)
episode 83
27.6%
the 12
 
4.0%
1 8
 
2.7%
3 8
 
2.7%
4 7
 
2.3%
2 6
 
2.0%
8 6
 
2.0%
a 6
 
2.0%
12 5
 
1.7%
5 5
 
1.7%
Other values (130) 155
51.5%
2023-08-05T14:25:04.565851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
178
 
11.7%
e 152
 
10.0%
i 120
 
7.9%
s 115
 
7.6%
o 108
 
7.1%
d 103
 
6.8%
p 88
 
5.8%
E 86
 
5.7%
a 55
 
3.6%
1 39
 
2.6%
Other values (65) 477
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 997
65.5%
Uppercase Letter 179
 
11.8%
Space Separator 178
 
11.7%
Decimal Number 150
 
9.9%
Other Punctuation 12
 
0.8%
Dash Punctuation 3
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 152
15.2%
i 120
12.0%
s 115
11.5%
o 108
10.8%
d 103
10.3%
p 88
8.8%
a 55
 
5.5%
r 35
 
3.5%
n 33
 
3.3%
l 33
 
3.3%
Other values (23) 155
15.5%
Uppercase Letter
ValueCountFrequency (%)
E 86
48.0%
C 11
 
6.1%
A 10
 
5.6%
T 8
 
4.5%
F 7
 
3.9%
M 6
 
3.4%
D 6
 
3.4%
N 6
 
3.4%
B 6
 
3.4%
J 5
 
2.8%
Other values (12) 28
 
15.6%
Decimal Number
ValueCountFrequency (%)
1 39
26.0%
2 29
19.3%
3 18
12.0%
5 16
10.7%
4 15
 
10.0%
8 8
 
5.3%
0 7
 
4.7%
7 7
 
4.7%
6 6
 
4.0%
9 5
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 5
41.7%
: 2
 
16.7%
' 2
 
16.7%
! 1
 
8.3%
# 1
 
8.3%
, 1
 
8.3%
Space Separator
ValueCountFrequency (%)
178
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1165
76.6%
Common 345
 
22.7%
Cyrillic 11
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 152
13.0%
i 120
10.3%
s 115
9.9%
o 108
 
9.3%
d 103
 
8.8%
p 88
 
7.6%
E 86
 
7.4%
a 55
 
4.7%
r 35
 
3.0%
n 33
 
2.8%
Other values (34) 270
23.2%
Common
ValueCountFrequency (%)
178
51.6%
1 39
 
11.3%
2 29
 
8.4%
3 18
 
5.2%
5 16
 
4.6%
4 15
 
4.3%
8 8
 
2.3%
0 7
 
2.0%
7 7
 
2.0%
6 6
 
1.7%
Other values (10) 22
 
6.4%
Cyrillic
ValueCountFrequency (%)
ы 1
9.1%
п 1
9.1%
у 1
9.1%
с 1
9.1%
к 1
9.1%
В 1
9.1%
е 1
9.1%
я 1
9.1%
и 1
9.1%
р 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1509
99.2%
Cyrillic 11
 
0.7%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
178
 
11.8%
e 152
 
10.1%
i 120
 
8.0%
s 115
 
7.6%
o 108
 
7.2%
d 103
 
6.8%
p 88
 
5.8%
E 86
 
5.7%
a 55
 
3.6%
1 39
 
2.6%
Other values (53) 465
30.8%
Cyrillic
ValueCountFrequency (%)
ы 1
9.1%
п 1
9.1%
у 1
9.1%
с 1
9.1%
к 1
9.1%
В 1
9.1%
е 1
9.1%
я 1
9.1%
и 1
9.1%
р 1
9.1%
None
ValueCountFrequency (%)
é 1
100.0%

season
Real number (ℝ)

Distinct12
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.902439
Minimum1
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:04.717357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile8
Maximum2022
Range2021
Interquartile range (IQR)1

Descriptive statistics

Standard deviation182.13863
Coefficient of variation (CV)9.6357213
Kurtosis122.87554
Mean18.902439
Median Absolute Deviation (MAD)0
Skewness11.082237
Sum2325
Variance33174.482
MonotonicityNot monotonic
2023-08-05T14:25:04.857816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 77
62.6%
2 20
 
16.3%
3 12
 
9.8%
5 4
 
3.3%
17 2
 
1.6%
8 2
 
1.6%
23 1
 
0.8%
15 1
 
0.8%
6 1
 
0.8%
2022 1
 
0.8%
Other values (2) 2
 
1.6%
ValueCountFrequency (%)
1 77
62.6%
2 20
 
16.3%
3 12
 
9.8%
5 4
 
3.3%
6 1
 
0.8%
7 1
 
0.8%
8 2
 
1.6%
15 1
 
0.8%
17 2
 
1.6%
23 1
 
0.8%
ValueCountFrequency (%)
2022 1
 
0.8%
29 1
 
0.8%
23 1
 
0.8%
17 2
 
1.6%
15 1
 
0.8%
8 2
 
1.6%
7 1
 
0.8%
6 1
 
0.8%
5 4
 
3.3%
3 12
9.8%

number
Real number (ℝ)

Distinct35
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.227642
Minimum1
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:05.005595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q316.5
95-th percentile59.2
Maximum328
Range327
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation47.323756
Coefficient of variation (CV)2.3395587
Kurtosis24.114014
Mean20.227642
Median Absolute Deviation (MAD)5
Skewness4.7443195
Sum2488
Variance2239.5379
MonotonicityNot monotonic
2023-08-05T14:25:05.159557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3 16
 
13.0%
1 10
 
8.1%
4 10
 
8.1%
2 9
 
7.3%
5 7
 
5.7%
8 7
 
5.7%
12 6
 
4.9%
6 6
 
4.9%
7 5
 
4.1%
13 4
 
3.3%
Other values (25) 43
35.0%
ValueCountFrequency (%)
1 10
8.1%
2 9
7.3%
3 16
13.0%
4 10
8.1%
5 7
5.7%
6 6
 
4.9%
7 5
 
4.1%
8 7
5.7%
9 2
 
1.6%
10 2
 
1.6%
ValueCountFrequency (%)
328 1
0.8%
272 1
0.8%
234 1
0.8%
159 1
0.8%
157 1
0.8%
92 1
0.8%
60 1
0.8%
52 2
1.6%
51 1
0.8%
50 1
0.8%

type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
regular
123 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters861
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular 123
100.0%

Length

2023-08-05T14:25:05.314095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-05T14:25:05.420494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
regular 123
100.0%

Most occurring characters

ValueCountFrequency (%)
r 246
28.6%
e 123
14.3%
g 123
14.3%
u 123
14.3%
l 123
14.3%
a 123
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 861
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 246
28.6%
e 123
14.3%
g 123
14.3%
u 123
14.3%
l 123
14.3%
a 123
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 861
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 246
28.6%
e 123
14.3%
g 123
14.3%
u 123
14.3%
l 123
14.3%
a 123
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 246
28.6%
e 123
14.3%
g 123
14.3%
u 123
14.3%
l 123
14.3%
a 123
14.3%

airdate
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2022-12-26 00:00:00
Maximum2022-12-26 00:00:00
2023-08-05T14:25:05.514443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:05.624863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

airtime
Date

MISSING 

Distinct16
Distinct (%)23.9%
Missing56
Missing (%)45.5%
Memory size1.1 KiB
Minimum2023-08-05 00:00:00
Maximum2023-08-05 22:30:00
2023-08-05T14:25:05.736904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:05.881894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
Distinct17
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2022-12-26 02:00:00+00:00
Maximum2022-12-27 06:00:00+00:00
2023-08-05T14:25:06.028447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:06.166598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

runtime
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)28.1%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean33.14876
Minimum2
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:06.306918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q112
median30
Q345
95-th percentile60
Maximum180
Range178
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.152967
Coefficient of variation (CV)0.78895762
Kurtosis12.859152
Mean33.14876
Median Absolute Deviation (MAD)15
Skewness2.843911
Sum4011
Variance683.97769
MonotonicityNot monotonic
2023-08-05T14:25:06.459097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
45 28
22.8%
10 20
16.3%
25 10
 
8.1%
30 7
 
5.7%
12 7
 
5.7%
19 6
 
4.9%
40 5
 
4.1%
29 4
 
3.3%
5 4
 
3.3%
60 3
 
2.4%
Other values (24) 27
22.0%
ValueCountFrequency (%)
2 1
 
0.8%
5 4
 
3.3%
7 1
 
0.8%
9 1
 
0.8%
10 20
16.3%
12 7
 
5.7%
15 1
 
0.8%
19 6
 
4.9%
20 1
 
0.8%
21 1
 
0.8%
ValueCountFrequency (%)
180 1
 
0.8%
165 1
 
0.8%
120 1
 
0.8%
89 1
 
0.8%
67 1
 
0.8%
65 1
 
0.8%
60 3
2.4%
58 1
 
0.8%
53 1
 
0.8%
52 1
 
0.8%

rating_average
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing123
Missing (%)100.0%
Memory size1.1 KiB

medium
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing90
Missing (%)73.2%
Memory size1.1 KiB
2023-08-05T14:25:06.707134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length73
Mean length73
Min length73

Characters and Unicode

Total characters2409
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/437/1092834.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/437/1092835.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/437/1092556.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/437/1092543.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/437/1092780.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092548.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/459/1149777.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092835.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092556.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092543.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092780.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092769.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092525.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092526.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/medium_landscape/437/1092527.jpg 1
 
3.0%
Other values (23) 23
69.7%
2023-08-05T14:25:07.099170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 231
 
9.6%
a 198
 
8.2%
m 165
 
6.8%
s 165
 
6.8%
t 165
 
6.8%
p 132
 
5.5%
e 132
 
5.5%
i 99
 
4.1%
c 99
 
4.1%
. 99
 
4.1%
Other values (22) 924
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1683
69.9%
Other Punctuation 363
 
15.1%
Decimal Number 330
 
13.7%
Connector Punctuation 33
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 198
11.8%
m 165
9.8%
s 165
9.8%
t 165
9.8%
p 132
 
7.8%
e 132
 
7.8%
i 99
 
5.9%
c 99
 
5.9%
d 99
 
5.9%
o 66
 
3.9%
Other values (8) 363
21.6%
Decimal Number
ValueCountFrequency (%)
4 49
14.8%
7 46
13.9%
1 40
12.1%
3 38
11.5%
2 36
10.9%
0 36
10.9%
9 34
10.3%
5 29
8.8%
8 15
 
4.5%
6 7
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 231
63.6%
. 99
27.3%
: 33
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1683
69.9%
Common 726
30.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 198
11.8%
m 165
9.8%
s 165
9.8%
t 165
9.8%
p 132
 
7.8%
e 132
 
7.8%
i 99
 
5.9%
c 99
 
5.9%
d 99
 
5.9%
o 66
 
3.9%
Other values (8) 363
21.6%
Common
ValueCountFrequency (%)
/ 231
31.8%
. 99
13.6%
4 49
 
6.7%
7 46
 
6.3%
1 40
 
5.5%
3 38
 
5.2%
2 36
 
5.0%
0 36
 
5.0%
9 34
 
4.7%
_ 33
 
4.5%
Other values (4) 84
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 231
 
9.6%
a 198
 
8.2%
m 165
 
6.8%
s 165
 
6.8%
t 165
 
6.8%
p 132
 
5.5%
e 132
 
5.5%
i 99
 
4.1%
c 99
 
4.1%
. 99
 
4.1%
Other values (22) 924
38.4%

original
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing90
Missing (%)73.2%
Memory size1.1 KiB
2023-08-05T14:25:07.362393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length75
Mean length75
Min length75

Characters and Unicode

Total characters2475
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/437/1092834.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/437/1092835.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/437/1092556.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/437/1092543.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/437/1092780.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/437/1092548.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/459/1149777.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092835.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092556.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092543.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092780.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092769.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092525.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092526.jpg 1
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/437/1092527.jpg 1
 
3.0%
Other values (23) 23
69.7%
2023-08-05T14:25:07.756065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 231
 
9.3%
t 198
 
8.0%
a 165
 
6.7%
s 132
 
5.3%
i 132
 
5.3%
o 132
 
5.3%
p 99
 
4.0%
c 99
 
4.0%
. 99
 
4.0%
g 99
 
4.0%
Other values (23) 1089
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1749
70.7%
Other Punctuation 363
 
14.7%
Decimal Number 330
 
13.3%
Connector Punctuation 33
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 198
 
11.3%
a 165
 
9.4%
s 132
 
7.5%
i 132
 
7.5%
o 132
 
7.5%
p 99
 
5.7%
c 99
 
5.7%
g 99
 
5.7%
m 99
 
5.7%
e 99
 
5.7%
Other values (9) 495
28.3%
Decimal Number
ValueCountFrequency (%)
4 49
14.8%
7 46
13.9%
1 40
12.1%
3 38
11.5%
0 36
10.9%
2 36
10.9%
9 34
10.3%
5 29
8.8%
8 15
 
4.5%
6 7
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 231
63.6%
. 99
27.3%
: 33
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1749
70.7%
Common 726
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 198
 
11.3%
a 165
 
9.4%
s 132
 
7.5%
i 132
 
7.5%
o 132
 
7.5%
p 99
 
5.7%
c 99
 
5.7%
g 99
 
5.7%
m 99
 
5.7%
e 99
 
5.7%
Other values (9) 495
28.3%
Common
ValueCountFrequency (%)
/ 231
31.8%
. 99
13.6%
4 49
 
6.7%
7 46
 
6.3%
1 40
 
5.5%
3 38
 
5.2%
0 36
 
5.0%
2 36
 
5.0%
9 34
 
4.7%
: 33
 
4.5%
Other values (4) 84
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 231
 
9.3%
t 198
 
8.0%
a 165
 
6.7%
s 132
 
5.3%
i 132
 
5.3%
o 132
 
5.3%
p 99
 
4.0%
c 99
 
4.0%
. 99
 
4.0%
g 99
 
4.0%
Other values (23) 1089
44.0%

_links_self
Text

UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:07.983219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters4797
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2460191
2nd rowhttps://api.tvmaze.com/episodes/2460192
3rd rowhttps://api.tvmaze.com/episodes/2410543
4th rowhttps://api.tvmaze.com/episodes/2463638
5th rowhttps://api.tvmaze.com/episodes/2330733
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2460191 1
 
0.8%
https://api.tvmaze.com/episodes/2472881 1
 
0.8%
https://api.tvmaze.com/episodes/2410543 1
 
0.8%
https://api.tvmaze.com/episodes/2463638 1
 
0.8%
https://api.tvmaze.com/episodes/2330733 1
 
0.8%
https://api.tvmaze.com/episodes/2513104 1
 
0.8%
https://api.tvmaze.com/episodes/2445911 1
 
0.8%
https://api.tvmaze.com/episodes/2462259 1
 
0.8%
https://api.tvmaze.com/episodes/2462260 1
 
0.8%
https://api.tvmaze.com/episodes/2454338 1
 
0.8%
Other values (113) 113
91.9%
2023-08-05T14:25:08.348849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 492
 
10.3%
p 369
 
7.7%
s 369
 
7.7%
e 369
 
7.7%
t 369
 
7.7%
o 246
 
5.1%
a 246
 
5.1%
i 246
 
5.1%
. 246
 
5.1%
m 246
 
5.1%
Other values (16) 1599
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3075
64.1%
Other Punctuation 861
 
17.9%
Decimal Number 861
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 369
12.0%
s 369
12.0%
e 369
12.0%
t 369
12.0%
o 246
8.0%
a 246
8.0%
i 246
8.0%
m 246
8.0%
h 123
 
4.0%
d 123
 
4.0%
Other values (3) 369
12.0%
Decimal Number
ValueCountFrequency (%)
4 190
22.1%
2 176
20.4%
7 80
9.3%
5 75
 
8.7%
3 74
 
8.6%
8 67
 
7.8%
6 63
 
7.3%
1 62
 
7.2%
9 46
 
5.3%
0 28
 
3.3%
Other Punctuation
ValueCountFrequency (%)
/ 492
57.1%
. 246
28.6%
: 123
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3075
64.1%
Common 1722
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 492
28.6%
. 246
14.3%
4 190
 
11.0%
2 176
 
10.2%
: 123
 
7.1%
7 80
 
4.6%
5 75
 
4.4%
3 74
 
4.3%
8 67
 
3.9%
6 63
 
3.7%
Other values (3) 136
 
7.9%
Latin
ValueCountFrequency (%)
p 369
12.0%
s 369
12.0%
e 369
12.0%
t 369
12.0%
o 246
8.0%
a 246
8.0%
i 246
8.0%
m 246
8.0%
h 123
 
4.0%
d 123
 
4.0%
Other values (3) 369
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 492
 
10.3%
p 369
 
7.7%
s 369
 
7.7%
e 369
 
7.7%
t 369
 
7.7%
o 246
 
5.1%
a 246
 
5.1%
i 246
 
5.1%
. 246
 
5.1%
m 246
 
5.1%
Other values (16) 1599
33.3%
Distinct58
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-08-05T14:25:08.607442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.943089
Min length32

Characters and Unicode

Total characters4175
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)27.6%

Sample

1st rowhttps://api.tvmaze.com/shows/41628
2nd rowhttps://api.tvmaze.com/shows/41628
3rd rowhttps://api.tvmaze.com/shows/54610
4th rowhttps://api.tvmaze.com/shows/55019
5th rowhttps://api.tvmaze.com/shows/62143
ValueCountFrequency (%)
https://api.tvmaze.com/shows/66444 12
 
9.8%
https://api.tvmaze.com/shows/52230 10
 
8.1%
https://api.tvmaze.com/shows/66535 6
 
4.9%
https://api.tvmaze.com/shows/66077 6
 
4.9%
https://api.tvmaze.com/shows/66256 6
 
4.9%
https://api.tvmaze.com/shows/66205 5
 
4.1%
https://api.tvmaze.com/shows/59543 5
 
4.1%
https://api.tvmaze.com/shows/64945 5
 
4.1%
https://api.tvmaze.com/shows/66180 4
 
3.3%
https://api.tvmaze.com/shows/66162 2
 
1.6%
Other values (48) 62
50.4%
2023-08-05T14:25:09.025908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 492
 
11.8%
s 369
 
8.8%
t 369
 
8.8%
h 246
 
5.9%
p 246
 
5.9%
a 246
 
5.9%
. 246
 
5.9%
o 246
 
5.9%
m 246
 
5.9%
6 178
 
4.3%
Other values (16) 1291
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2706
64.8%
Other Punctuation 861
 
20.6%
Decimal Number 608
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 369
13.6%
t 369
13.6%
h 246
9.1%
p 246
9.1%
a 246
9.1%
o 246
9.1%
m 246
9.1%
e 123
 
4.5%
w 123
 
4.5%
c 123
 
4.5%
Other values (3) 369
13.6%
Decimal Number
ValueCountFrequency (%)
6 178
29.3%
4 87
14.3%
5 77
12.7%
2 55
 
9.0%
0 50
 
8.2%
3 41
 
6.7%
1 40
 
6.6%
9 34
 
5.6%
7 28
 
4.6%
8 18
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 492
57.1%
. 246
28.6%
: 123
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2706
64.8%
Common 1469
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 492
33.5%
. 246
16.7%
6 178
 
12.1%
: 123
 
8.4%
4 87
 
5.9%
5 77
 
5.2%
2 55
 
3.7%
0 50
 
3.4%
3 41
 
2.8%
1 40
 
2.7%
Other values (3) 80
 
5.4%
Latin
ValueCountFrequency (%)
s 369
13.6%
t 369
13.6%
h 246
9.1%
p 246
9.1%
a 246
9.1%
o 246
9.1%
m 246
9.1%
e 123
 
4.5%
w 123
 
4.5%
c 123
 
4.5%
Other values (3) 369
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 492
 
11.8%
s 369
 
8.8%
t 369
 
8.8%
h 246
 
5.9%
p 246
 
5.9%
a 246
 
5.9%
. 246
 
5.9%
o 246
 
5.9%
m 246
 
5.9%
6 178
 
4.3%
Other values (16) 1291
30.9%

summary
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing92
Missing (%)74.8%
Memory size1.1 KiB
2023-08-05T14:25:09.312599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length407
Median length158
Mean length180.74194
Min length63

Characters and Unicode

Total characters5603
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row<p>Squad 10 Captain Isshin Shiba of the Thirteen Court Guard Squads decides to not report his encounter with the Quincy named Masaki Kurosaki in the World of the Living to the Head Captain.</p>
2nd row<p>Ichigo decides to return to Hoohden with a renewed resolve to finish what he has started.</p>
3rd row<p>The beautiful, historic watermill in Lower Blissingham is home to award-winning artisan baker Tom Larkton and his wife, pastry chef Chrissie. The mill has been in Chrissie"s family for centuries, and the couple have restored it and turned it into a thriving business with an online following. But their popularity has overshadowed Lower Blissingham, and many of the villagers are not happy about it….</p>
4th row<p>Da fakes his own death to cash in on life insurance. Uncle Andy's worst nightmare comes true when his dole is stopped, and he gets a new job as a security guard at Larne Harbour.</p>
5th row<p>Yo peoples, my sincerest apologies for the delays on Fridays episode, will have the bonus Christmas special drop on Wednesday hopefully at 5ish, then you'll get another episode on Friday. Separately, I took my own advice and skated down to Archway, as directed in episode 3, and it was a miiisteak. Don't do it, wharrever you do, jas don't do it.</p>
ValueCountFrequency (%)
and 43
 
4.5%
to 43
 
4.5%
the 37
 
3.9%
a 21
 
2.2%
his 16
 
1.7%
in 14
 
1.5%
for 14
 
1.5%
of 12
 
1.3%
their 10
 
1.1%
is 10
 
1.1%
Other values (498) 726
76.7%
2023-08-05T14:25:09.771509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
912
16.3%
e 496
 
8.9%
a 364
 
6.5%
t 338
 
6.0%
i 328
 
5.9%
s 315
 
5.6%
n 310
 
5.5%
o 304
 
5.4%
r 245
 
4.4%
h 220
 
3.9%
Other values (57) 1771
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4205
75.0%
Space Separator 915
 
16.3%
Uppercase Letter 178
 
3.2%
Other Punctuation 162
 
2.9%
Math Symbol 128
 
2.3%
Dash Punctuation 9
 
0.2%
Decimal Number 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 496
11.8%
a 364
 
8.7%
t 338
 
8.0%
i 328
 
7.8%
s 315
 
7.5%
n 310
 
7.4%
o 304
 
7.2%
r 245
 
5.8%
h 220
 
5.2%
l 169
 
4.0%
Other values (16) 1116
26.5%
Uppercase Letter
ValueCountFrequency (%)
S 24
13.5%
J 18
 
10.1%
A 16
 
9.0%
B 15
 
8.4%
M 13
 
7.3%
C 11
 
6.2%
O 10
 
5.6%
L 10
 
5.6%
T 8
 
4.5%
H 8
 
4.5%
Other values (12) 45
25.3%
Other Punctuation
ValueCountFrequency (%)
. 52
32.1%
, 47
29.0%
/ 32
19.8%
' 26
16.0%
2
 
1.2%
; 1
 
0.6%
" 1
 
0.6%
? 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
6 2
33.3%
0 1
16.7%
1 1
16.7%
5 1
16.7%
3 1
16.7%
Space Separator
ValueCountFrequency (%)
912
99.7%
  3
 
0.3%
Math Symbol
ValueCountFrequency (%)
< 64
50.0%
> 64
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
88.9%
1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4383
78.2%
Common 1220
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 496
 
11.3%
a 364
 
8.3%
t 338
 
7.7%
i 328
 
7.5%
s 315
 
7.2%
n 310
 
7.1%
o 304
 
6.9%
r 245
 
5.6%
h 220
 
5.0%
l 169
 
3.9%
Other values (38) 1294
29.5%
Common
ValueCountFrequency (%)
912
74.8%
< 64
 
5.2%
> 64
 
5.2%
. 52
 
4.3%
, 47
 
3.9%
/ 32
 
2.6%
' 26
 
2.1%
- 8
 
0.7%
  3
 
0.2%
6 2
 
0.2%
Other values (9) 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5597
99.9%
None 3
 
0.1%
Punctuation 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
912
16.3%
e 496
 
8.9%
a 364
 
6.5%
t 338
 
6.0%
i 328
 
5.9%
s 315
 
5.6%
n 310
 
5.5%
o 304
 
5.4%
r 245
 
4.4%
h 220
 
3.9%
Other values (54) 1765
31.5%
None
ValueCountFrequency (%)
  3
100.0%
Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%

Interactions

2023-08-05T14:25:01.140682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:58.944413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.504596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.059981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.612463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:01.253743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.062621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.618661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.172513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.721431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:01.363402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.172338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.724741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.284574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.827649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:01.472275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.288891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.840795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.394230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.937514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:01.575742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.394928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:24:59.945806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:00.498413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:25:01.034603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-05T14:25:09.889749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idid_embeddedseasonnumberruntime
id1.0000.784-0.196-0.103-0.353
id_embedded0.7841.000-0.494-0.111-0.233
season-0.196-0.4941.000-0.090-0.178
number-0.103-0.111-0.0901.000-0.163
runtime-0.353-0.233-0.178-0.1631.000

Missing values

2023-08-05T14:25:01.740664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-05T14:25:02.004265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-05T14:25:02.171398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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